diff --git a/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures.py b/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures.py
index c122443..dc558c1 100644
--- a/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures.py
+++ b/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures.py
@@ -215,10 +215,11 @@ class AttackInputData:
   entropy_test: Optional[np.ndarray] = None
 
   # If loss is not explicitly specified, this function will be used to derive
-  # loss from logits and labels. It can be a pre-defined `LossFunction`.
+  # loss from logits and labels. It can be a pre-defined `LossFunction` or its
+  # string representation, or a callable.
   # If a callable is provided, it should take in two argument, the 1st is
   # labels, the 2nd is logits or probs.
-  loss_function: Union[Callable[[np.ndarray, np.ndarray], np.ndarray],
+  loss_function: Union[Callable[[np.ndarray, np.ndarray], np.ndarray], str,
                        utils.LossFunction] = utils.LossFunction.CROSS_ENTROPY
   # Whether `loss_function` will be called with logits or probs. If not set
   # (None), will decide by availablity of logits and probs and logits is
diff --git a/tensorflow_privacy/privacy/privacy_tests/utils.py b/tensorflow_privacy/privacy/privacy_tests/utils.py
index b09384a..d448f95 100644
--- a/tensorflow_privacy/privacy/privacy_tests/utils.py
+++ b/tensorflow_privacy/privacy/privacy_tests/utils.py
@@ -146,7 +146,7 @@ def string_to_loss_function(string: str):
 def get_loss(loss: Optional[np.ndarray], labels: Optional[np.ndarray],
              logits: Optional[np.ndarray], probs: Optional[np.ndarray],
              loss_function: Union[Callable[[np.ndarray, np.ndarray],
-                                           np.ndarray], LossFunction],
+                                           np.ndarray], LossFunction, str],
              loss_function_using_logits: Optional[bool],
              multilabel_data: Optional[bool]) -> Optional[np.ndarray]:
   """Calculates (if needed) losses.
@@ -176,6 +176,9 @@ def get_loss(loss: Optional[np.ndarray], labels: Optional[np.ndarray],
     raise ValueError('We need probs to compute loss, but it is set to None.')
 
   predictions = logits if loss_function_using_logits else probs
+
+  if isinstance(loss_function, str):
+    loss_function = string_to_loss_function(loss_function)
   if loss_function == LossFunction.CROSS_ENTROPY:
     if multilabel_data:
       loss = multilabel_bce_loss(labels, predictions,
diff --git a/tensorflow_privacy/privacy/privacy_tests/utils_test.py b/tensorflow_privacy/privacy/privacy_tests/utils_test.py
index b65a3b9..df097f6 100644
--- a/tensorflow_privacy/privacy/privacy_tests/utils_test.py
+++ b/tensorflow_privacy/privacy/privacy_tests/utils_test.py
@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from unittest import mock
+
 from absl.testing import absltest
 from absl.testing import parameterized
 import numpy as np
@@ -198,5 +200,39 @@ class TestMultilabelBCELoss(parameterized.TestCase):
                            pred, from_logits)
 
 
+class TestGetLoss(parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      ('xe', utils.LossFunction.CROSS_ENTROPY, False),
+      ('xe str', 'cross_entropy', False),
+      ('xe multi', utils.LossFunction.CROSS_ENTROPY, True),
+      ('xe multi str', 'cross_entropy', True),
+      ('sq', utils.LossFunction.SQUARED, False),
+      ('sq str', 'squared', False),
+  )
+  @mock.patch.object(utils, 'squared_loss')
+  @mock.patch.object(utils, 'multilabel_bce_loss')
+  @mock.patch.object(utils, 'log_loss')
+  def test_get_loss_call_loss_function(self, loss_function, multilabel_data,
+                                       mock_log_loss, mock_multilabel_bce_loss,
+                                       mock_squared_loss):
+    """Test if get_loss calls the correct loss_function."""
+    utils.get_loss(
+        loss=None,
+        labels=np.array([0]),
+        logits=np.array([[0.1, -0.1]]),
+        probs=None,
+        loss_function=loss_function,
+        loss_function_using_logits=True,
+        multilabel_data=multilabel_data)
+    if loss_function in ['cross_entropy', utils.LossFunction.CROSS_ENTROPY]:
+      if not multilabel_data:
+        mock_log_loss.assert_called_once()
+      else:
+        mock_multilabel_bce_loss.assert_called_once()
+    else:
+      mock_squared_loss.assert_called_once()
+
+
 if __name__ == '__main__':
   absltest.main()